A word or phrase can often be associated with more than one possible meaning. These meanings may be referred to as senses of the word, or word sense. A natural language processing system can treat a word according to one or more of its word senses. A word sense can have one or more hypernyms. A hypernym generally has a broader, or more generic, meaning than its hyponym. For example “blue” is a hypernym for senses of the words “navy,” “aqua,” and “cyan.” Also, “color” is a hypernym for a sense of the word “blue.” Thus, “blue” is a hyponym of “color.” In addition to multiple senses of a word, a natural language processing system can treat a word according to one or more of the hypernyms of the sense of the word.
Unfortunately, processing every word in terms of all of the word senses and hypernyms associated with the word can generate a considerable increase in complexity and resource requirements. Techniques for word sense disambiguation (WSD) attempt to reduce ambiguity between the senses of a word. Thus, WSD techniques may also reduce complexity and resource requirements within a natural language processing system.
In an information search application, a search index entry can be created for every sense of a word encountered in the content to be indexed for search. Similarly, a search index entry can be created for every hypernym of every sense of a word encountered in the content to be indexed for search. Either, or both, of these indexing approaches can introduce, to the search index, the considerable computational impact discussed above. Thus, the search index can become inefficient or unreasonable to manage. Moreover, increased complexity may be incurred during a query of the search index due to additional word senses to be processed.
It is with respect to these considerations and others that the disclosure made herein is presented.